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Netlab Reference Manual gtm
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<H1> gtm
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<h2>
Purpose
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Create a Generative Topographic Map.

<p><h2>
Synopsis
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<PRE>
net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc)
net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)
</PRE>


<p><h2>
Description
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<p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc)</CODE>,
takes the dimension of the latent space <CODE>dimlatent</CODE>, the
number of data points sampled in the latent space <CODE>nlatent</CODE>, the
dimension of the data space <CODE>dimdata</CODE>, the number of centres in the
RBF model <CODE>ncentres</CODE>, the activation function for the RBF
<CODE>rbfunc</CODE>
and returns a data structure <CODE>net</CODE>. The parameters in the
RBF and GMM sub-models are set by calls to the corresponding creation routines
<CODE>rbf</CODE> and <CODE>gmm</CODE>.

<p>The fields in <CODE>net</CODE> are
<PRE>
  type = 'gtm'
  nin = dimension of data space
  dimlatent = dimension of latent space
  rbfnet = RBF network data structure
  gmmnet = GMM data structure
  X = sample of latent points
</PRE>


<p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)</CODE>,
 sets a Gaussian zero mean prior on the
parameters of the RBF model. <CODE>prior</CODE> must be a scalar and represents
the inverse variance of the prior distribution.  This gives rise to
a weight decay term in the error function.

<p><h2>
See Also
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<CODE><a href="gtmfwd.htm">gtmfwd</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="gmm.htm">gmm</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
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<p>Copyright (c) Ian T Nabney (1996-9)


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